Selectively Remove Column Values in R Data Frame - r

Question

Example
Suppose in the famous iris data set, I have determined that when Sepal.Length > 5.0, there was an error in my measurement device.
In this contrived example, I would like to keep the Sepal.Length column with its original value, but change the remaining columns to NA if the Sepal.Length > 5.0 for that row.
As an example, this:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
Would become this:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 NA NA NA NA
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 NA 1.7 NA NA
I could certain do this manually via vectorization. Something along the lines of:
iris$Sepal.Width <- ifelse(iris$Sepal.Length > 5.0, NA, iris$Sepal.Width)
In this approach however, I would need to manually specify every column.
Question
I strongly suspect there is a clever way to tackle this via either purrr or dplyr. Nevertheless, I've gotten myself down a pmap / modify_at rabbit hole. Any suggestions towards elegance would be much appreciated.
Thanks!

Answer 1

library(data.table)
dt <- copy(iris)
setDT(dt)
dt[Sepal.Length > 5.0, (which(!names(dt) == "Sepal.Length")) := NA]
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1: 5.1 NA NA NA NA
# 2: 4.9 3.0 1.4 0.2 setosa
# 3: 4.7 3.2 1.3 0.2 setosa
# 4: 4.6 3.1 1.5 0.2 setosa
# 5: 5.0 3.6 1.4 0.2 setosa
# ---
# 146: 6.7 NA NA NA NA
# 147: 6.3 NA NA NA NA
# 148: 6.5 NA NA NA NA
# 149: 6.2 NA NA NA NA
# 150: 5.9 NA NA NA NA

Answer 2

Alternative would be to simply use this (this is only handy if you are interested in all columns, beginning with the second one)
iris[iris$Sepal.Length > 5.0, 2:ncol(iris)] <- NA
# And the output for first six rows
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 NA NA NA <NA>
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 NA NA NA <NA>

Answer 3

It sounds like this would work for you
my_clip <- function(x, z) ifelse(z>5, NA, x)
iris %>% mutate_at(vars(-Sepal.Length), my_clip, z=.$Sepal.Length)
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1 5.1 NA NA NA NA
# 2 4.9 3.0 1.4 0.2 1
# 3 4.7 3.2 1.3 0.2 1
# 4 4.6 3.1 1.5 0.2 1
# 5 5.0 3.6 1.4 0.2 1
# 6 5.4 NA NA NA NA
We use mutate_at to grab all the column we want to transform and then since you can't reference other columns easily in your mutate_at function, we need to pass in the threshold column as a separate parameter using the .$ syntax.

Answer 4

Since you asked for a purrr example, here goes. Although I prefer the data.table answer already proposed:
library(purrr)
library(tidyr)
iris %>% nest(-Sepal.Length) %>%
mutate(data = ifelse(Sepal.Length > 5.0,
map(data, function(x) x*NA), data)) %>%
unnest

Answer 5

With magrittr you could do this :
library(magrittr)
iris %>% head %>% inset(.$Sepal.Length > 5,-1,NA)
or using base R instead of magrittr (same output, just uglier function :), and you still need magrittr or dplyr for the pipes):
iris %>% head %>% `[<-`(.$Sepal.Length > 5,-1,NA)
-1 is the index of the column you want to keep, negated.
result
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1 5.1 NA NA NA <NA>
# 2 4.9 3.0 1.4 0.2 setosa
# 3 4.7 3.2 1.3 0.2 setosa
# 4 4.6 3.1 1.5 0.2 setosa
# 5 5.0 3.6 1.4 0.2 setosa
# 6 5.4 NA NA NA <NA>